Field of the Invention
The invention relates to a method for generating at least one combination image dataset from at least two parallel datasets, of the type wherein a coil array is used to record the parallel datasets, with a raw dataset being acquired with each coil of the coil array during a parallel recording of measuring signals and the raw datasets of a parallel recording forming a parallel dataset and with the raw datasets each containing a reduced dataset and at least one calibration data item.
Description of the Prior Art
It is known that a number of image datasets acquired using a magnetic resonance system can be combined to form a combination image dataset. For example, two image datasets recorded with different echo times can be combined to form a water image or a fat image. This so-called Dixon technique can therefore be used for water or fat suppression. The echo times of the image datasets, which are otherwise recorded using identical parameter sets, can be selected in such a manner that the water and fat spins are sometimes “in-phase” and sometimes “out-of-phase”.
It is also known that T1, T2, and T2* maps can be recorded, with image datasets with varying echo times being fitted in the T2 and T2* maps, while the repetition time is changed between the image datasets in the T1 maps. The combination image dataset is obtained here by way of an exponential fit.
In this process individual image datasets can be recorded successively, they can be recorded segmented or even line by line in k-space. At the end of the data acquisition, the k-space lines associated with an image dataset are assigned to it and the combination image dataset is determined from the image datasets.
A number of data recording sequences are thus available in each instance. For example both gradient echo-based and spin echo-based sequences are known for recording T1 maps. With the Dixon technique any sequences such as FLASH, spin echo or turbo spin echo, also referred to as RARE, can be used, as long as two different echo times can be implemented.
It is also known that measuring signals can be recorded at the same time with a number of coils, in order thus to shorten measuring time. These methods are known as PPA or partial parallel acquisition. Only some of the measuring signals required to reconstruct an image dataset are recorded but with a number of coils in each instance. These coils form what is known as a coil array. If the measuring signals acquired with a single coil are used for reconstruction, under-sampling results in what is known as aliasing or foldover artifact. Each coil therefore records a reduced dataset.
A number of reconstruction methods are known for obtaining a single artifact-free dataset from these reduced datasets. Such basic reconstruction methods are known by the acronyms GRAPPA (GeneRalized Autocalibrating Partially Parallel Acquisition), SENSE (SENSitivity Encoding for fast MRI) and SMASH (SiMultaneous Acquisition of Spatial Harmonics). Modifications are also known, which are described in conjunction with the basic reconstruction methods.
With the reconstruction method SENSE (SENSE: sensitivity encoding for fast MRI. Pruessmann K P, Weiger M, Scheidegger M B, Boesiger P, Magn Reson Med., 42(5), 952-62, 1999) coil sensitivities are measured in order to determine a pseudoinverse matrix therefrom. A single overall image with or without significantly reduced aliasing artifact is determined from the individual images containing aliasing artifacts using said matrix. In other words the coil images are unfolded to form an overall image. SENSE therefore operates in the image space.
In contrast to SMASH (Sodickson, Manning: Simultaneous Acquisition of Spatial Harmonics (SMASH): Fast Imaging with Radiofrequency Coil Arrays, Magn. Res. Med., 38: 591-603, 1997), the measured measuring signals are combined linearly taking into account coil sensitivities to achieve modulation, as when switching a phase-encode gradient. The missing k-space data, in particular k-space lines, are therefore calculated. SMASH therefore operates in the k-space.
One version of this is Auto-SMASH (Jakob et al, AUTO-SMASH: a self-calibrating technique for SMASH imaging, MAGMA, 7: 42-54, 1998). One problem with SMASH is the determination of the coil sensitivity profiles. Therefore additional k-space lines, known as auto calibration signals (ACS), which are positioned at intermediate positions in the k-space, are recorded. The required coil weighting factors are determined by a fit between lines measured in the “standard” manner and ACS lines. There is therefore no need to determine the coil sensitivity profiles.
A further development of AUTO-SMASH, VD-AUTO-SMASH (Heidemann et al, Variable Density AUTO-SMASH (VD-AUTO-SMASH), Proceedings of the eighth Scientific Meeting of the International Society of Magnetic Resonance in Medicine, p. 274, 2000), uses the ACS lines.
With GRAPPA as well, the missing k-space lines are reconstructed before the image dataset is generated, so that a complete dataset is available for the creation of the image dataset. Here as well, the raw datasets have what are known as ACS lines or auto calibration signals for auto calibration in addition to the reduced dataset. In contrast to SMASH techniques however images can be obtained using a square sum reconstruction, thereby improving the SNR.
Should one wish to accelerate the methods cited in the introduction for generating a combination image using PPA methods, an intermediate step should be included, in which intermediate datasets are first generated from raw datasets, with a combination image dataset being determined therefrom. Depending on the PPA method the intermediate dataset may or may not have already undergone a Fourier transform. As described above, the raw datasets are incomplete in respect of foldover artifacts. In the present application the raw datasets acquired during a parallel recording are referred to as a parallel dataset.
A problem that occurs in this context is that during the creation of combination image datasets using parallel imaging artifacts such as what is known as a third arm artifact, spoiling or flow artifacts and even foldover artifacts occur.